학술논문
How to get the most from microarray data: advice from reverse genomics
Document Type
Academic Journal
Author
Source
BMC Genomics. March 21, 2014, Vol. 15
Subject
Language
English
ISSN
1471-2164
Abstract
Author(s): Ivan P Gorlov[sup.1] , Ji-Yeon Yang[sup.2] , Jinyoung Byun[sup.3] , Christopher Logothetis[sup.1] , Olga Y Gorlova[sup.4] , Kim-Anh Do[sup.5] and Christopher Amos[sup.3] Background Global profiling of gene expression by [...]
Background Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data-derived predictor of known cancer associated genes. Results We found that the traditional approach of identifying cancer genes--identifying differentially expressed genes--is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011-2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples. Conclusions The observation that the high interindividual variation of gene expression in tumor tissues is the best predictor of cancer-associated genes is likely a result of tumor heterogeneity on gene level. Computer simulation demonstrates that in the case of heterogeneity, an assessment of variance in tumors provides a better identification of cancer genes than does the comparison of the expression in normal and tumor tissues. Our results thus challenge the current paradigm that comparing the mean expression between normal and tumorous tissues is the best approach to identifying cancer-associated genes; we found that the high interindividual variation in expression is a better approach, and that using variation would improve our chances of identifying cancer-associated genes. Keywords: Gene expression, Cancer genes, Interindividual variation in gene expression
Background Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data-derived predictor of known cancer associated genes. Results We found that the traditional approach of identifying cancer genes--identifying differentially expressed genes--is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011-2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples. Conclusions The observation that the high interindividual variation of gene expression in tumor tissues is the best predictor of cancer-associated genes is likely a result of tumor heterogeneity on gene level. Computer simulation demonstrates that in the case of heterogeneity, an assessment of variance in tumors provides a better identification of cancer genes than does the comparison of the expression in normal and tumor tissues. Our results thus challenge the current paradigm that comparing the mean expression between normal and tumorous tissues is the best approach to identifying cancer-associated genes; we found that the high interindividual variation in expression is a better approach, and that using variation would improve our chances of identifying cancer-associated genes. Keywords: Gene expression, Cancer genes, Interindividual variation in gene expression